Application of a wavelet-based algorithm on HS-SPME/GC signals for the classification of balsamic vinegars

A novel feature selection and classification algorithm (WPTER) based on the wavelet packet transform has been applied to the discrimination of balsamic vinegars, namely the typical made Aceto Balsamico Tradizionale di Modena, which gained the PDO denomination on the year 2000, from the industrial made Aceto Balsamico of the Modena district. All the samples have been characterized on the basis of the gas chromatographic (GC) profiles of the headspace (HS) volatile fraction, sampled by solid phase microextraction (SPME). Good discrimination between the two categories has been obtained both for the calibration and for the test set samples. GC-MS analysis allowed the identification of the peaks lying in the chromatographic regions selected by the algorithm, giving useful suggestions about the compounds which may be worth of further investigation in order to rationalize the chemical transformation occurring during the traditional making procedure. The proposed methodology seems very promising in authentication tasks, coupling some of the advantages of blind analysis with the possibility of acquiring chemical information, and giving, at the same time, very parsimonious multivariate classification models, which can be particularly suitable for data storage and handling.